regQTLs: Single nucleotide polymorphisms that modulate microRNA regulation of gene expression in tumors

Single nucleotide polymorphism-based biomarker in primary hypertension

Primary hypertension is a multiplex and multifactorial disease influenced by various strong components including genetics. Extensive research such as Genome-wide association studies and candidate gene studies have revealed various single nucleotide polymorphisms (SNPs) related to hypertension, providing insights into the genetic basis of the condition. This review summarizes the current status of SNP research in primary hypertension, including examples of hypertension-related SNPs, their location, function, and frequency in different populations. The potential clinical implications of SNP research for primary hypertension management are also discussed, including disease risk prediction, personalized medicine, mechanistic understanding, and lifestyle modifications. Furthermore, this review highlights emerging technologies and methodologies that have the potential to revolutionize the vast understanding of the basis of genetics in primary hypertension. Gene editing holds the potential to target and correct any kind of genetic mutations that contribute to the development of hypertension or modify genes involved in blood pressure regulation to prevent or treat the condition. Advances in computational biology and machine learning enable researchers to analyze large datasets and identify complex genetic interactions contributing to hypertension risk. In conclusion, SNP research in primary hypertension is rapidly evolving with emerging technologies and methodologies that have the potential to transform the knowledge about genetic basis related to the condition. These advances hold promise for personalized prevention and treatment strategies tailored to an individual's genetic profile ultimately improving patient outcomes and reducing healthcare costs.

Exploring noncoding variants in genetic diseases: from detection to functional insights

Previous studies on genetic diseases predominantly focused on protein-coding variations, overlooking the vast noncoding regions in the human genome. The development of high-throughput sequencing technologies and functional genomics tools has enabled the systematic identification of functional noncoding variants. These variants can impact gene expression, regulation, and chromatin conformation, thereby contributing to disease pathogenesis. Understanding the mechanisms that underlie the impact of noncoding variants on genetic diseases is indispensable for the development of precisely targeted therapies and the implementation of personalized medicine strategies. The intricacies of noncoding regions introduce a multitude of challenges and research opportunities. In this review, we introduce a spectrum of noncoding variants involved in genetic diseases, along with research strategies and advanced technologies for their precise identification and in-depth understanding of the complexity of the noncoding genome. We will delve into the research challenges and propose potential solutions for unraveling the genetic basis of rare and complex diseases.

miRNAs are now starring in "No Time to Die: Overcoming the chemoresistance in cancer"

Cancer is a leading cause of death globally, with about 19.3 million new cases reported each year. Current therapies for cancer management include-chemotherapy, radiotherapy, and surgery. However, they are loaded with side effects and tend to cause toxicity in the patient's body posttreatment, ultimately hindering the response towards the treatment building up resistance. This is where noncoding RNAs such as miRNAs help provide us with a helping hand for taming the chemoresistance and providing potential holistic cancer management. MicroRNAs are promising targets for anticancer therapy as they perform critical regulatory roles in various signaling cascades related to cell proliferation, apoptosis, migration, and invasion. Combining miRNAs and anticancer drugs and devising a combination therapy has managed cancer well in various independent studies. This review aims to provide insights into how miRNAs play a mechanistic role in cancer development and progression and regulate drug resistance in various types of cancers. Furthermore, next-generation novel therapies using miRNAs in combination with anticancer treatments in multiple cancers have been put forth and how they improve the efficacy of the treatments. Exemplary studies currently in the preclinical and clinical models have been summarized. Ultimately, we briefly talk through the challenges that come forward with it and minimize them.

Genetic Variants of MIR27A, MIR196A2 May Impact the Risk for the Onset of Coronary Artery Disease in the Pakistani Population

Genetic variants in microRNA genes have a detrimental effect on miRNA-mediated regulation of gene expression and may contribute to coronary artery disease (CAD). CAD is the primary cause of mortality worldwide. Several environmental, genetic, and epigenetic factors are responsible for CAD susceptibility. The contribution of protein-coding genes is extensively studied. However, the role of microRNA genes in CAD is at infancy. The study is aimed to investigate the impact of rs895819, rs11614913, and rs2168518 variants in MIR27A, MIR196A2, and MIR4513, respectively, in CAD using allele-specific PCR. Results: For variant rs11614913, significant distribution of the genotypes among the cases and controls was determined by co-dominant [χ² = 54.4; p value ≤ 0.0001], dominant (C/C vs. C/T + T/T) [OR = 0.257 (0.133-0.496); p value ≤ 0.0001], recessive (T/T vs. C/T + C/C) [OR = 1.56 (0.677-0.632); p value = 0.398], and additive models [OR = 0.421 (0.262-0.675); p value = 0.0004]. Similarly, a significant association of rs895819 was determined by co-dominant [χ² = 9.669; p value ≤ 0.008], dominant (A/A vs. A/G + G/G) [OR = 0.285 (0.1242-0.6575); p value ≤ 0.0034], recessive (G/G vs. A/G + A/A) [OR = 0.900 (0.3202-3.519); p value = 1.000], and additive models [OR = 0.604 (0.3640-1.002); p value = 0.05] while no significant association of rs2168518 with CAD was found. Conclusion: The variants rs895819 and rs11614913 are the susceptibility factors for CAD.

Exploring the effects of genetic variation on gene regulation in cancer in the context of 3D genome structure

Background

Numerous genome-wide association studies (GWAS) conducted to date revealed genetic variants associated with various diseases, including breast and prostate cancers. Despite the availability of these large-scale data, relatively few variants have been functionally characterized, mainly because the majority of single-nucleotide polymorphisms (SNPs) map to the non-coding regions of the human genome. The functional characterization of these non-coding variants and the identification of their target genes remain challenging.

Results

In this communication, we explore the potential functional mechanisms of non-coding SNPs by integrating GWAS with the high-resolution chromosome conformation capture (Hi-C) data for breast and prostate cancers. We show that more genetic variants map to regulatory elements through the 3D genome structure than the 1D linear genome lacking physical chromatin interactions. Importantly, the association of enhancers, transcription factors, and their target genes with breast and prostate cancers tends to be higher when these regulatory elements are mapped to high-risk SNPs through spatial interactions compared to simply using a linear proximity. Finally, we demonstrate that topologically associating domains (TADs) carrying high-risk SNPs also contain gene regulatory elements whose association with cancer is generally higher than those belonging to control TADs containing no high-risk variants.

Conclusions

Our results suggest that many SNPs may contribute to the cancer development by affecting the expression of certain tumor-related genes through long-range chromatin interactions with gene regulatory elements. Integrating large-scale genetic datasets with the 3D genome structure offers an attractive and unique approach to systematically investigate the functional mechanisms of genetic variants in disease risk and progression.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-021-01021-x.

Analyzing Association between Expression Quantitative Trait and CNV for Breast Cancer Based on Gene Interaction Network Clustering and Group Sparse Learning

Aims:

The occurrence and development of tumor is accompanied by the change of pathogenic gene expression. Tumor cells avoid the damage of immune cells by regulating the expression of immune related genes.

Background:

Tracing the causes of gene expression variation is helpful to understand tumor evolution and metastasis.

Objective:

Current gene expression variation explanation methods are confronted with several main challenges: low explanation power, insufficient prediction accuracy, and lack of biological meaning.

Method:

In this study, we propose a novel method to analyze the mRNA expression variations of breast cancers risk genes. Firstly, we collected some high-confidence risk genes related to breast cancer and then designed a rank-based method to preprocess the breast cancers copy number variation (CNV) and mRNA data. Secondly, to elevate the biological meaning and narrow down the combinatorial space, we introduced a prior gene interaction network and applied a network clustering algorithm to generate high density subnetworks. Lastly, to describe the interlinked structure within and between subnetworks and target genes mRNA expression, we proposed a group sparse learning model to identify CNVs for pathogenic genes expression variations.

Result:

The performance of the proposed method is evaluated by both significantly improved predication accuracy and biological meaning of pathway enrichment analysis.

Conclusion:

The experimental results show that our method has practical significance

The miR-196a SNP Rs11614913 but not the miR-499 rs37464444 SNP is a Risk Factor for Non-small Cell Lung Cancer in an Iranian Population

Background

Globally, lung cancer represents a major cause of cancer-related deaths. The regulation of gene expression is modulated by small noncoding RNAs called miRNAs that can act as both tumor suppressors and oncogenes. The maturation, expression and binding to target mRNAs is affected by single nucleotide polymorphisms (SNPs) in miRNA genomic regions thereby contributing to cancer susceptibility. SNPs Rs11614913 in miR196a and Rs3746444 in miR-499 are implicated in the development of cancers such as non-small cell lung cancer (NSCLC) in non-Arabic subjects.

Materials and Methods

A small cohort of 204 participants including 104 lung cancer patients and 100 non-cancer controls subjects were enrolled into the study. The allele frequencies were determined by Polymerase Chain Reaction- Restriction Fragment Length Polymorphism (PCR-RFLP) and their correlation with lung cancer risk was determined.

Results

The miR-196a rs11614913 polymorphism increased the risk of NSCLC (CC vs. TT+TC: OR= 2.26, 95%CI= 1.28 - 3.98, P= 0.0046) in a dominant genetic model. No statistically significant association was found between the miR-499 rs37464444 polymorphism and NSCLC.

Conclusion

The rs11614913 polymorphism in miR-196a, but not the miR-499 rs37464444 polymorphism, increased the risk of NSCLC. Further studies with larger sample sizes in correlation with functional outcomes at the cellular level should be undertaken.

Multiplex-Heterogeneous Network-Based Capturing Potential SNP "Switches" of Pathways Associating With Diverse Disease Characteristics of Asthma

Asthma is a complex heterogeneous respiratory disorder. In recent years nubbly regions of the role of genetic variants and transcriptome including mRNAs, microRNAs, and long non-coding RNAs in the pathogenesis of asthma have been separately excavated and reported. However, how to systematically integrate and decode this scattered information remains unclear. Further exploration would improve understanding of the internal communication of asthma. To excavate new insights into the pathogenesis of asthma, we ascertained three asthma characteristics according to reviews, airway inflammation, airway hyperresponsiveness, and airway remodeling. We manually created a contemporary catalog of corresponding risk transcriptome, including mRNAs, miRNAs, and lncRNAs. MIMP is a multiplex-heterogeneous networks-based approach, measuring the relevance of disease characteristics to the pathway by examining the similarity between the determined vectors of risk transcriptome and pathways in the same low-dimensional vector space. It was developed to enable a more concentrated and in-depth exploration of potential pathways. We integrated experimentally validated competing endogenous RNA regulatory information and the SNPs with significant pathways into the ceRNA-mediated SNP switching pathway network (CSSPN) to analyze ceRNA regulation of pathways and the role of SNP in these dysfunctions. We discovered 11 crucial ceRNA regulations concerning asthma disease feature pathway and propose a potential mechanism of ceRNA regulatory SNP → gene → pathway → disease feature effecting asthma pathogenesis, especially for MALAT1 (rs765499057/rs764699354/rs189435941) → hsa-miR-155 → IL13 (rs201185816/rs1000978586/rs202101165) → Interleukin-4 and Interleukin-13 signaling → inflammation/airway remodeling and MALAT1 (rs765499057/rs764699354/rs189435941) → hsa-miR-155 → IL17RB (rs948046241) → Interleukin-17 signaling (airway remodeling)/Cytokine-cytokine receptor interaction (inflammation). This study showed a systematic and propagable workflow for capturing the potential SNP “switch” of asthma through text and database mining and provides further information on the pathogenesis of asthma.

A novel regQTL-SNP and the risk of lung cancer: a multi-dimensional study

RegQTL, a novel concept, indicates that different genotypes of some SNPs have differential effects on the expression patterns of miRNAs and their target mRNAs. We aimed to identify the association between regQTL-SNPs and lung cancer risk and to explore the underlying mechanisms. The two-stage case-control study included the first stage in a Chinese population (626 lung cancer cases and 667 healthy controls) and the second stage in a European population (18,082 lung cancer cases and 13,780 healthy controls). Functional annotations were conducted based on the GTEx and the TCGA databases. Functional experiments were performed to explore the underlying biological mechanisms in vitro and vivo. After strict screening, five candidate regQTL-SNPs (rs7110737, rs273957, rs6593210, rs3768617, and rs6836432) were selected. Among them, the variant T allele of rs3768617 in LAMC1 was found to significantly increase the risk of lung cancer (first stage: P = 0.044; second stage: P = 0.007). The eQTL analysis showed that LAMC1 expression level was significantly higher in subjects with the variant T allele of rs3768617 (P = 1.10 × 10^-14). In TCGA paired database, the regQTL annotation indicated the different expression patterns between LAMC1 and miRNA-548b-3p for the distinct genotypes of rs3768617. Additionally, LAMC1 knockdown significantly inhibited malignant phenotypes in lung cancer cell lines and suppressed tumor growth. A novel regQTL-SNP, rs3768617, might affect lung cancer risk by modulating the expression patterns of miRNA-548b-3p and LAMC1. RegQTL-SNPs could provide a new perspective for evaluating the regulatory function of SNPs in lung cancer development.

Methylation and expression levels of microRNA-23b/-24-1/-27b, microRNA-30c-1/-30e, microRNA-301a and let-7g are dysregulated in clear cell renal cell carcinoma

BACKGROUND

Renal cell carcinoma is the most common form of kidney cancer in adults. DNA methylation of regulatory sequences at the genomic level and interaction between microRNAs and the messenger RNAs of target genes at the posttranscriptional level contribute to the dynamic regulation of gene activity. Aberrations in these mechanisms can result in impaired functioning of cell signaling pathways, such as that observed in malignant tumors. We hypothesized that microRNA genes methylation may be associated with renal cancer in patients.

METHODS AND RESULTS

We examined methylation levels of 22 microRNA genes in tumor and normal kidney tissue of 30 patients with TNM Stage III clear cell renal cell carcinoma using a pathway-specific real-time polymerase chain reaction array (EpiTect Methyl II PCR Arrays, Qiagen). MicroRNA expression analysis by quantitative polymerase chain reaction was also performed. Significant differences in methylation levels were found in two genes and in two clusters of microRNA genes. MicroRNA-23b/-24-1/-27b, microRNA -30c-1/-30e and let-7 g was hypermetylated in clear cell renal cell carcinoma tissue, microRNA -301a was hypomethylated in tumor compared with the adjacent normal tissues. Expression of microRNA-301a, microRNA-23b in the clear cell renal cell carcinoma tissues was significantly overexpressed when compared with the adjacent normal tissues and let-7 g was significantly downregulated in tumor.

CONCLUSIONS

Our results may indicate the contribution of microRNA-301a, microRNA-23b and let-7 g in the pathogenesis of renal cancer, but further studies are needed to determine the functional significance of the detected changes.

S100B gene polymorphisms are associated with the S100B level and Alzheimer's disease risk by altering the miRNA binding capacity

To examine the role of S100B in genetic susceptibility to Alzheimer’s disease (AD), we conducted a case-control study to analyze four polymorphism loci (rs2839364, rs1051169, rs2300403, and rs9722) of the S100B gene and AD risk. We found an independent increased risk of AD in ApoE ε4(-) subjects carrying the rs9722 AA-genotype (OR = 2.622, 95% CI = 1.399–4.915, P = 0.003). Further investigation revealed the serum S100B levels to be lower in rs9722 GG carriers than in rs9722 AA carriers (P = 0.003). We identified three miRNAs (miR-340-3p, miR-593-3p, miR-6827-3p) in which the seed match region covered locus rs9722. Luciferase assays indicated that the rs9722 G allele has a higher binding affinity to miR-6827-3p than the rs9722 A allele, leading to a significantly decreased fluorescence intensity. Subsequent western blot analysis showed that the S100B protein level of SH-SY5Y cells, which carry the rs9722 G allele, decreased significantly following miR-6827-3p stimulation (P = 0.009). The present study suggests that the rs9722 polymorphism may upregulate the expression of S100B by altering the miRNA binding capacity and may thus increase the AD risk. This finding would be of great help for the early diagnosis of AD.

MicroRNA-mediated regulation of agronomically important seed traits: a treasure trove with shades of grey!

Seed development is an intricate process with multiple levels of regulation. MicroRNAs (miRNAs) have emerged as one of the crucial components of molecular networks underlying agronomically important seed traits in diverse plant species. In fact, loss of function of the genes regulating miRNA biogenesis also exhibits defects in seed development. A total of 21 different miRNAs have experimentally been shown to regulate seed size, nutritional content, vigor, and shattering, and have been reviewed here. The mechanism details of the associated regulatory cascades mediated through transcriptional regulators, phytohormones, basic metabolic machinery, and secondary siRNAs are elaborated. Co-localization of miRNAs and their target regions with seed-related QTLs provides new avenues for engineering these traits using conventional breeding programs or biotechnological interventions. While global analysis of miRNAs using small RNA sequencing studies are expanding the repertoire of candidate miRNAs, recent revelations on their inheritance, transport, and mechanism of action would be instrumental in designing better strategies for optimizing agronomically relevant seed traits.

The miR-146a SNP Rs2910164 and miR-155 SNP rs767649 Are Risk Factors for Non-Small Cell Lung Cancer in the Iranian Population

Background

Lung cancer is one of the leading causes of death worldwide. MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression and may act as both tumor suppressors and as oncogenes. The presence of single nucleotide polymorphisms (SNPs) inside the miRNA genomic region could affect target miRNA maturation, expression, and binding to its target mRNA and contribute to cancer development. Previous studies on the SNPs Rs2910164 in miR-146a and Rs767649 in miR-155 showed association with non-small cell lung cancer (NSCLC) development. Thus, the aim of this study was to detect any correlation between those SNPs in Iranian NSCLC patients.

Methods

In a small cohort study, 165 NSCLC patients and 147 noncancer controls were enrolled between Apr 2015 and Sep 2019 at the Masih Daneshvari Hospital, Tehran, Iran. Allele frequencies from the genomic DNA of blood cells were studied using PCR-RFLP and their association with the risk of lung cancer was evaluated.

Results

The rs2910164C allele (OR = 1.56, 95% CI = 1.10-2.21, p = 0.012) and CC genotype (OR = 2.93, 95% CI = 1.07-7.9, p = 0.034, respectively) were associated with a significantly increased risk for lung cancer compared to that for the GG genotype. When patients were stratified according to smoking exposure, no association with rs2910164 variants was found. The AT genotype (OR = 0.57, 95% CI = 0.33-0.99, p = 0.048) and the A allele frequency (OR = 0.58, 95% CI = 0.35-0.98, p = 0.043) in rs767649 were lower in NSCLC patients in comparison with the control group. In addition, the rs767649 AT genotype frequency in smoking controls was higher than in smoking NSCLC patients (OR = 0.44, 95% CI = 0.21-0.90, p = 0.024). No association was found between rs2910164 and rs767649 variants and stage or type of NSCLC.

Conclusion

Our finding suggests that miR-146a rs2910164 and miR-155 rs767649 polymorphisms may be considered as genetic risk factors for the susceptibility to NSCLC in the Iranian population. However, a larger multicenter study across Iran is needed to confirm these findings.

An 8 miRNA-Based Risk Score System for Predicting the Prognosis of Patients With Papillary Thyroid Cancer

Background:

Dysregulation of microRNAs (miRNAs) in papillary thyroid cancer (PTC) might influence prognosis of PTC. This study is aimed to develop a risk score system for predicting prognosis of PTC.

Methods:

The miRNA and gene expression profiles of PTC were obtained from The Cancer Genome Atlas database. PTC samples were randomly separated into training set (n = 248) and validation set (n = 248). The differentially expressed miRNAs (DE-miRNAs) in the training set were screened using limma package. The independent prognosis-associated DE-miRNAs were identified for building a risk score system. Risk score of PTC samples in the training set was calculated and samples were divided into high risk group and low risk group. Kaplan-Meier curves and receiver operating characteristic (ROC) curve were used to assess the accuracy of the risk score system in the training set, validation set and entire set. Finally, a miRNA-gene regulatory network was visualized by Cytoscape software, followed by enrichment analysis.

Results:

Totally, 162 DE-miRNAs between tumor and control groups in the training set were identified. An 8 independent prognosis-associated DE-miRNAs, (including miR-1179, miR-133b, miR-3194, miR-3912, miR-548j, miR-6720, miR-6734, and miR-6843) based risk score system was developed. The area under ROC curve in the training set, validation set and entire set was all above 0.93. A miRNA-gene regulatory network involving the 8 DE-miRNAs were built and functional enrichment analysis suggested the genes in the network were significantly enriched into 13 pathways, including calcium signaling pathway and hedgehog signaling pathway.

Conclusion:

The risk score system developed this study might be used for predicting the prognosis of PTC. Besides, the 8 miRNAs might affect the prognosis of PTC via hedgehog signaling pathway and calcium signaling pathway.

Exploring the effects of genetic variation on gene regulation in cancer in the context of 3D genome structure.. [DOI: 10.1101/2020.10.06.328567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Numerous genome-wide association studies (GWAS) conducted to date revealed genetic variants associated with various diseases, including breast and prostate cancers. Despite the availability of these large-scale data, relatively few variants have been functionally characterized, mainly because the majority of single-nucleotide polymorphisms (SNPs) map to the non-coding regions of the human genome. The functional characterization of these non-coding variants and the identification of their target genes remain challenging. In this communication, we explore the potential functional mechanisms of non-coding SNPs by integrating GWAS with the high-resolution chromosome conformation capture (Hi-C) data for breast and prostate cancers. We show that more genetic variants map to regulatory elements through the 3D genome structure than the 1D linear genome lacking physical chromatin interactions. Importantly, the association of enhancers, transcription factors, and their target genes with breast and prostate cancers tends to be higher when these regulatory elements are mapped to high-risk SNPs through spatial interactions compared to simply using a linear proximity. Finally, we demonstrate that topologically associating domains (TADs) carrying high-risk SNPs also contain gene regulatory elements whose association with cancer is generally higher than those belonging to control TADs containing no high-risk variants. Our results suggest that many SNPs may contribute to the cancer development by affecting the expression of certain tumor-related genes through long-range chromatin interactions with gene regulatory elements. Integrating large-scale genetic datasets with the 3D genome structure offers an attractive and unique approach to systematically investigate the functional mechanisms of genetic variants in disease risk and progression.

Critical Analysis of Genome-Wide Association Studies: Triple Negative Breast Cancer Quae Exempli Causa

Genome-wide association studies (GWAS) are useful in assessing and analyzing either differences or variations in DNA sequences across the human genome to detect genetic risk factors of diseases prevalent within a target population under study. The ultimate goal of GWAS is to predict either disease risk or disease progression by identifying genetic risk factors. These risk factors will define the biological basis of disease susceptibility for the purposes of developing innovative, preventative, and therapeutic strategies. As single nucleotide polymorphisms (SNPs) are often used in GWAS, their relevance for triple negative breast cancer (TNBC) will be assessed in this review. Furthermore, as there are different levels and patterns of linkage disequilibrium (LD) present within different human subpopulations, a plausible strategy to evaluate known SNPs associated with incidence of breast cancer in ethnically different patient cohorts will be presented and discussed. Additionally, a description of GWAS for TNBC will be presented, involving various identified SNPs correlated with miRNA sites to determine their efficacies on either prognosis or progression of TNBC in patients. Although GWAS have identified multiple common breast cancer susceptibility variants that individually would result in minor risks, it is their combined effects that would likely result in major risks. Thus, one approach to quantify synergistic effects of such common variants is to utilize polygenic risk scores. Therefore, studies utilizing predictive risk scores (PRSs) based on known breast cancer susceptibility SNPs will be evaluated. Such PRSs are potentially useful in improving stratification for screening, particularly when combining family history, other risk factors, and risk prediction models. In conclusion, although interpretation of the results from GWAS remains a challenge, the use of SNPs associated with TNBC may elucidate and better contextualize these studies.

The Role of Noncoding Variants in Heritable Disease

The genetic basis of disease has largely focused on coding regions. However, it has become clear that a large proportion of the noncoding genome is functional and harbors genetic variants that contribute to disease etiology. Here, we review recent examples of inherited noncoding alterations that are responsible for Mendelian disorders or act to influence complex traits. We explore both rare and common genetic variants and discuss the wide range of mechanisms by which they affect gene regulation to promote disease. We also debate the challenges and progress associated with identifying and interpreting the functional and clinical significance of genetic variation in the context of the noncoding regulatory landscape.

Association of KLK3, VAMP8 and MDM4 Genetic Variants within microRNA Binding Sites with Prostate Cancer: Evidence from Serbian Population

A growing number of studies have suggested that genetic variants affecting the micro-RNA- binding mechanisms (miRSNPs) constitute a promising novel class of biomarkers for prostate cancer (PCa) biology. Among the most extensively studied miRSNPs in the context of cancer is the variation rs4245739 in the MDM4 gene, while a recent large-scale analysis revealed significant differences in genotype distributions between aggressive and non-aggressive disease for rs1058205 in KLK3 and rs1010 in VAMP8. In this study, we examined a total of 1083 subjects for these three variants using Taqman® SNP Genotyping Assays. Three hundred and fifty-five samples of peripheral blood were obtained from patients with PCa and 358 samples from patients with benign prostatic hyperplasia (BPH). The control group consisted of 370 healthy volunteers. Comparisons of genotype distributions among PCa and BPH patients, as well as between PCa patients and healthy controls, yielded no evidence of association between the analyzed genetic variants and the risk of developing PCa. However, all three tested genetic variants have shown the association with the parameters of PCa progression. For KLK3 variant rs1058205, minor allele C was found to associate with the lower serum PSA score in PCa patients (PSA > 20 ng/ml vs. PSA < 10 ng/ml comparison, Prec = 0.038; ORrec = 0.20, 95%CI 0.04-1.05). The obtained results point out the potential relevance of the tested genetic variants for the disease aggressiveness assessment.

Two-stage Bayesian GWAS of 9576 individuals identifies SNP regions that are targeted by miRNAs inversely expressed in Alzheimer's and cancer

INTRODUCTION

We compared genetic variants between Alzheimer's disease (AD) and two age-related cancers-breast and prostate -to identify single-nucleotide polymorphisms (SNPs) that are associated with inverse comorbidity of AD and cancer.

METHODS

Bayesian multinomial regression was used to compare sex-stratified cases (AD and cancer) against controls in a two-stage study. A ±500 KB region around each replicated hit was imputed and analyzed after merging individuals from the two stages. The microRNAs (miRNAs) that target the genes involving these SNPs were analyzed for miRNA family enrichment.

RESULTS

We identified 137 variants with inverse odds ratios for AD and cancer located on chromosomes 19, 4, and 5. The mapped miRNAs within the network were enriched for miR-17 and miR-515 families.

DISCUSSION

The identified SNPs were rs4298154 (intergenic), within TOMM40/APOE/APOC1, MARK4, CLPTM1, and near the VDAC1/FSTL4 locus. The miRNAs identified in our network have been previously reported to have inverse expression in AD and cancer.

The role of dietary phytochemicals in the carcinogenesis via the modulation of miRNA expression

PURPOSE

Phytochemicals are naturally occurring plant-derived compounds and some of them have the potential to serve as anticancer drugs. Based on recent evidence, aberrantly regulated expression of microRNAs (miRNAs) is closely associated with malignancy. MicroRNAs are characterized as small non-coding RNAs functioning as posttranscriptional regulators of gene expression. Accordingly, miRNAs regulate various target genes, some of which are involved in the process of carcinogenesis.

RESULTS

This comprehensive review emphasizes the anticancer potential of phytochemicals, either isolated or in combination, mediated by miRNAs. The ability to modulate the expression of miRNAs demonstrates their importance as regulators of tumorigenesis. Phytochemicals as anticancer agents targeting miRNAs are widely studied in preclinical in vitro and in vivo research. Unfortunately, their anticancer efficacy in targeting miRNAs is less investigated in clinical research.

CONCLUSIONS

Significant anticancer properties of phytochemicals as regulators of miRNA expression have been proven, but more studies investigating their clinical relevance are needed.

Characterising the genetic basis of immune response variation to identify causal mechanisms underlying disease susceptibility

Over the last 10 years, genome-wide association studies (GWAS) have identified hundreds of susceptibility loci for autoimmune diseases. However, despite increasing power for the detection of both common and rare coding variants affecting disease susceptibility, a large fraction of disease heritability has remained unexplained. In addition, a majority of the identified loci are located in noncoding regions, and translation of disease-associated loci into new biological insights on the etiology of immune disorders has been lagging. This highlights the need for a better understanding of noncoding variation and new strategies to identify causal genes at disease loci. In this review, I will first detail the molecular basis of gene expression and review the various mechanisms that contribute to alter gene activity at the transcriptional and post-transcriptional level. I will then review the findings from 10 years of functional genomics studies regarding the genetics on gene expression, in particular in the context of infection. Finally, I will discuss the extent to which genetic variants that modulate gene expression at transcriptional and post-transcriptional level contribute to disease susceptibility and present strategies to leverage this information for the identification of causal mechanisms at disease loci in the era of whole genome sequencing.